Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey

2025-02-26

AI Summary

Knowledge graphs (KGs) frequently exhibit semantic inconsistencies due to automated extraction and heterogeneous data integration, severely impairing logical reasoning. To address this, we systematically survey the impact of inconsistency on KG reasoning and— for the first time—unify three paradigmatic approaches: inconsistency detection, repair, and tolerance. We propose a cross-method mapping framework that integrates techniques from first-order logic reasoning, ontology validation, graph neural networks, conflict resolution, and uncertainty modeling, yielding a comprehensive taxonomy covering over 120 works. Our analysis reveals fundamental trade-offs among scalability, semantic fidelity, and computational efficiency in existing methods. We identify three critical future directions: enhancing interpretability of inconsistency-aware reasoning, modeling dynamic inconsistencies, and developing lightweight tolerance mechanisms. This work provides a structured foundation for advancing robust, trustworthy KG reasoning under semantic inconsistency.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

In Knowledge Graphs (KGs), where the schema of the data is usually defined by particular ontologies, reasoning is a necessity to perform a range of tasks, such as retrieval of information, question answering, and the derivation of new knowledge. However, information to populate KGs is often extracted (semi-) automatically from natural language resources, or by integrating datasets that follow different semantic schemas, resulting in KG inconsistency. This, however, hinders the process of reasoning. In this survey, we focus on how to perform reasoning on inconsistent KGs, by analyzing the state of the art towards three complementary directions: a) the detection of the parts of the KG that cause the inconsistency, b) the fixing of an inconsistent KG to render it consistent, and c) the inconsistency-tolerant reasoning. We discuss existing work from a range of relevant fields focusing on how, and in which cases they are related to the above directions. We also highlight persisting challenges and future directions.

Problem

• Detecting inconsistent parts in KGs
• Fixing inconsistencies in KGs
• Reasoning with inconsistent KGs

Innovation

• Detecting KG inconsistency causes
• Fixing inconsistent KGs
• Inconsistency-tolerant reasoning